1. Field of the Invention
The present invention is directed generally to a system for recommending musical compositions to a user and more particularly to a system for determining a current mood of a user and recommending musical compositions to the user based on the user's current mood.
2. Description of the Related Art
With the advancement of information technology listening to music using a computer has become a common practice. In fact, the impact of digital music has been so great that it has become a primary focus of many businesses. Numerous efforts have been made to improve the experience of listening music on a computer. One of such efforts involves automatically recommending songs to the listener.
All music/song recommendation systems use four broad categories of inputs for their recommendations: (1) metadata such as artist, album, genre, etc.; (2) acoustic features such as beats, melody, etc.; (3) direct feedback from the users such as rating; and (4) collaborative feedback such as information obtained from other users including purchasing patterns, listening patterns, and the like. A typical music recommendation system uses all or some of these inputs in various combinations. The most advanced recommendation systems typically use all of the above inputs and weigh their contribution to the music recommendation process. Nevertheless, these systems suffer from many disadvantages and shortcomings.
Liking or disliking a song is an expression of human mind. Since mood is an emotional state of human mind, it has an immense role in deciding when a person likes or dislikes something. The primary objective of a music recommendation system is to predict songs that a listener would like and hence it would be beneficial for a recommendation system to consider the mood of the listener when recommending songs. A major disadvantage of most prior art music recommendation systems is that they fail to consider a listener's mood.
However, a few recommendation systems exist that consider the mood of a listener and appreciate that mood has a great deal of influence in determining a listener's liking or disliking of songs. Unfortunately, most of these systems do not attempt to capture and quantify/qualify mood as an essential feature. Thus, these systems fail to use the effect of mood in their recommendation system. Systems that do consider mood as an essential feature use very intrusive methods such as taking a picture of the listener to capture the listener's mood. Such systems are likely to encounter greater difficulty in practical implementation due to security and privacy concerns.
Another shortcoming of most music recommendation systems is that they depend on collective generalization. In other words, data are collected from all users of a recommendation system and generalized before the data are applied and used to generate a recommendation to an individual user. Since the success of a recommendation depends on the opinion of an individual user, it is important to consider a specific individual's likes or dislikes. Because the collective generalization approach fails to consider the individual user's likes and dislikes, this approach will often make inappropriate recommendations to the user and fail to recommend choices preferred by the user.
Yet another shortcoming of many of the existing recommendation systems is that their recommendations depend on the user's direct feedback. For example, many systems ask users to rate songs. Most listeners do not rate songs because they (1) want to enjoy music and are not willing to do any extra work, and (2) listen to music in the background while focusing primarily on some other task. Since very few listeners actually take part in providing direct feedback, any recommendation that depends on such feedback provided by a very small proportion of the users fail to generate accurate recommendations.
Thus, a need exists for recommendation systems that overcome the above shortcomings. For example, a need exists for a music recommendation system that uses various inputs (such as metadata, acoustic features, etc.) and does require user input beyond observing user interaction with the system related to the selection and playback of songs. A music recommendation that considers the user's mood would also be desirable. Moreover, a need exists for a system that learns which songs to recommend based on an individual listener's mood and is capable of improving its recommendations over time.
The present application provides these and other advantages as will be apparent from the following detailed description and accompanying figures.